SummarySeveral candidate gene studies on the metabolic syndrome (MetS) have been conducted. However, for most single nucleotide polymorphisms (SNPs) no systematic review on their association with MetS exists. A systematic electronic literature search was conducted until the 2nd of June 2010, using HuGE Navigator. English language articles were selected. Only genes of which at least one SNP-MetS association was studied in an accumulative total population Ն4000 subjects were included. Meta-analyses were conducted on SNPs with three or more studies available in a generally healthy population. In total 88 studies on 25 genes were reviewed. Additionally, for nine SNPs in seven genes (GNB3, PPARG, TCF7L2, APOA5, APOC3, APOE, CETP) a meta-analysis was conducted. The minor allele of rs9939609 (FTO), rs7903146 (TCF7L2), C56G (APOA5), T1131C (APOA5), C482T (APOC3), C455T (APOC3) and 174G>C (IL6) were more prevalent in subjects with MetS, whereas the minor allele of Taq-1B (CETP) was less prevalent in subjects with the MetS. After having systematically reviewed the most studied SNP-MetS associations, we found evidence for an association with the MetS for eight SNPs, mostly located in genes involved in lipid metabolism.

OBJECTIVEMetabolic syndrome (MetS) is a cluster of abdominal obesity, hyperglycemia, hypertension, and dyslipidemia, which increases the risk for type 2 diabetes and cardiovascular diseases (CVDs). Some argue that MetS is not a single disorder because the traditional MetS features do not represent one entity, and they would like to exclude features from MetS. Others would like to add additional features in order to increase predictive ability of MetS. The aim of this study was to identify a MetS model that optimally predicts type 2 diabetes and CVD while still representing a single entity.RESEARCH DESIGN AND METHODSIn a random sample (n = 1,928) of the EPIC-NL cohort and a subset of the EPIC-NL MORGEN study (n = 1,333), we tested the model fit of several one-factor MetS models using confirmatory factor analysis. We compared predictive ability for type 2 diabetes and CVD of these models within the EPIC-NL case-cohort study of 545 incident type 2 diabetic subjects, 1,312 incident CVD case subjects, and the random sample, using survival analyses and reclassification.RESULTSThe standard model, representing the current MetS definition (EPIC-NL comparative fit index [CFI] = 0.95; MORGEN CFI = 0.98); the standard model excluding blood pressure (EPIC-NL CFI = 0.95; MORGEN CFI = 1.00); and the standard model extended with hsCRP (EPIC-NL CFI = 0.95) had an acceptable model fit. The model extended with hsCRP predicted type 2 diabetes (integral discrimination index [IDI]: 0.34) and CVD (IDI: 0.07) slightly better than did the standard model.CONCLUSIONSIt seems valid to represent the traditional MetS features by a single entity. Extension of this entity with hsCRP slightly improves predictive ability for type 2 diabetes and CVD.

BackgroundMechanisms involved in metabolic syndrome (MetS) development include insulin resistance, weight regulation, inflammation and lipid metabolism. Aim of this study is to investigate the association of single nucleotide polymorphisms (SNPs) involved in these mechanisms with MetS.MethodsIn a random sample of the EPIC-NL study (n = 1886), 38 SNPs associated with waist circumference, insulin resistance, triglycerides, HDL cholesterol and inflammation in genome wide association studies (GWAS) were selected from the 50K IBC array and one additional SNP was measured with KASPar chemistry. The five groups of SNPs, each belonging to one of the metabolic endpoints mentioned above, were associated with MetS and MetS-score using Goeman’s global test. For groups of SNPs significantly associated with the presence of MetS or MetS-score, further analyses were conducted.ResultsThe group of waist circumference SNPs was associated with waist circumference (P=0.03) and presence of MetS (P=0.03). Furthermore, the group of SNPs related to insulin resistance was associated with MetS score (P<0.01), HDL cholesterol (P<0.01), triglycerides (P<0.01) and HbA1C (P=0.04). Subsequent analyses showed that MC4R rs17782312, involved in weight regulation, and IRS1 rs2943634, related to insulin resistance were associated with MetS (OR 1.16, 95%CI 1.02-1.32 and OR 0.88, 95% CI 0.79; 0.97, respectively). The groups of inflammation and lipid SNPs were neither associated with presence of MetS nor with MetS score.ConclusionsIn this study we found support for the hypothesis that weight regulation and insulin metabolism are involved in MetS development.MC4R rs17782312 and IRS1 rs2943634 may explain part of the genetic variation in MetS.